Chat Notification Scoring to Reduce User Toil
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Solution Overview
Problem
In team-based perpetual chat platforms, the excessive number of notifications in large channels causes unnecessary toil for users, as all accounts are often notified of messages, leading to disruptive and repetitive work, especially when only a subset of accounts is likely to respond with relevant information.
Innovation Solution
A computer-implemented method using a learning model to determine the likelihood of accounts responding with relevant information by generating scores for each account, recommending only the most relevant accounts for notification, and allowing the message to be posted while at-mentioning them, thereby reducing unnecessary notifications and allowing all users to view the message without interruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all accounts are notified of messages in large channels, then all users can access the message, but excessive notifications cause unnecessary toil and disruptions
Solution Approach 1:
The patent segments the notification delivery process by dividing accounts into different notification groups based on their likelihood to respond. Instead of notifying all accounts uniformly, the system categorizes accounts and applies different notification strategies to each segment, thereby reducing unnecessary disruptions while maintaining message accessibility for those who need to see it.
Solution Approach 2:
The system employs a learning model that automatically analyzes message content and account characteristics to determine notification priorities without manual intervention. The model self-adjusts notification strategies based on historical data and patterns, reducing toil by eliminating the need for users to manually manage notification preferences or for administrators to configure notification rules for each message.
2Productivity
If a learning model is used to generate scores and recommend accounts for notification, then notification efficiency is improved, but system complexity increases
Solution Approach 1:
The learning model serves multiple functions within the chat system: it categorizes messages, generates response likelihood scores for each account, recommends notification targets, and continuously learns from interactions. This multi-functionality consolidates what could be separate complex systems into a single unified model, improving notification efficiency without proportionally increasing overall system complexity.
Solution Approach 2:
The learning model acts as an intermediary layer between message sending and notification delivery. It processes message content and account data to generate scores and recommendations, which then guide the notification system. This intermediary approach simplifies the overall architecture by centralizing the decision-making logic in a single model rather than requiring complex rules and configurations across multiple system components.
3Object-affected harmful factors
If notifications are targeted to specific accounts, then unnecessary toil is reduced, but some relevant responses may be missed
Solution Approach 1:
The notification targeting is dynamic rather than static. The learning model continuously adjusts which accounts receive notifications based on the specific message content, the sender's intent, and the historical behavior patterns of accounts. This dynamic approach ensures that notification targets are optimized for each individual message, reducing unnecessary toil while maintaining high reliability by adapting to different contexts and message types.
Solution Approach 2:
The system incorporates feedback loops where the learning model analyzes actual response patterns from notified and non-notified accounts. This feedback is used to continuously refine and improve the scoring and recommendation algorithms. By learning from past outcomes, the system becomes increasingly accurate at identifying which accounts are most likely to provide relevant responses, thereby reducing unnecessary notifications while maintaining response completeness.
Data Source
AI summary
A computer-implemented method reduces toil in chat systems. The method includes receiving, from a sending account, a message to be sent in a chat environment, where the chat environment includes a plurality of accounts including the sending account. The method also includes determining, by a learning model, a category of the message. The method further includes generating, for each account of the plurality accounts in a channel of the chat environment, a score, where the score indicates a likelihood an associated account will respond to a post with relevant information. The method includes recommending, based on the score, one or more accounts of the plurality of accounts to an at message, where the at message is configured to cause a notification to the associated account. The method further includes posting the message in the channel of the chat environment while at-mentioning the one or more accounts.